Abstract In order to forecast and make provisions for future demand of motorcycle in Akure, there is a need to understand factors driving motorcycle ownership, therefore, this study examines factors which affect motorcycle ownership in Akure metropolis. Three different zones namely, Low Density (LD), Medium Density (MD) and High Density (HD) were considered,, both close-ended and open-ended questionnaire were administered to 900 households representing 75% of the total population. Using Statistical Package for Social Sciences version 16 data were analyzed and binomial logistic regression analysis was used in developing a model which showed that only academic qualification of household head, number of household members and average monthly income of household significantly influences motorcycle ownership across the zones at the 95% confidence level. Both average monthly income of household and academic qualification of household head had a negative influence on motorcycle ownership whereas number of household members had a positive relationship with motorcycle ownership. The results shows that there is a 1.43 times likelihood of owning a motorcycle with a unit increase in the number of household members, while there is a reduction in the likelihood of owning a motorcycle by 1.66 times and 2.17 times for a unit increase in the average monthly income and academic qualification of household head respectively. The results obtained can be used in developing policy framework to improve public transport and control motorcycle ownership in the city of Akure.

Abstract Predictive models for entry flow at rotary intersections in Akure - a developing city in Nigeria- have been developed. Data were collected at the intersections critical to traffic flow in the study area using a cine camera placed at a vantage point from the road sections during peak and off-peak periods in week days. Entry flow (qe) was modelled as a function of circulating flow (qc), delay (da), headway (h) and geometric features of the intersections. The data were fitted to a multiple linear regression equation to obtain the generalized flow models for peak and off peak periods. The equations obtained were validated using empirical data other than those used to calibrate the model. The adjusted R2 values obtained during the peak and off peak periods were 95.8% and 87.7% respectively, indicating that the independent variables (circulating flow, delay and headway) made significant contributions in predicting the entry flow. The models developed can be used to evaluate entry flow at rotary intersections in the study area and other cities in developing countries with similar traffic characteristics for which such models are scarce, thereby facilitating planning and design of effective traffic control mechanisms.